Existing document ranking techniques in information filtering assign ranks, such as scores, rates, or probabilities, to documents based on the relevance or similarity of these documents to user profiles. The relevance of each document to a user's profile is typically determined by either the Euclidian distance of the document from the user profile or the probability of the document being relevant to the user profile. Document ranks are computed independently for each document. In addition, rank computations treat all components of a user profile in a uniform way. Once relevant documents are identified, these documents are presented to the user in descending order based on their relevance rank.
However, presenting relevant documents to a user in decreasing document relevance order determined as discussed above does not always result in a document collection that best matches a user's information needs. For example, when many documents are assigned very similar relevance ranks and, in addition, when the user can receive only a subset of these documents, then selecting and supplying the highest ranked (in absolute terms) documents is not always the best approach.
A method is needed for ranking documents so that their relative order achieves the maximum effectiveness with respect to a user's information needs.